Intelligent computing in large-scale systems provides systematic methodologies and tools for building complex inferential systems, which are able to adapt, mine data sets, evolve, and act in a nimble manner within major distributed environments with diverse architectures featuring multiple cores, accelerators, and high-speed networks.

We believe that the papers presented in this special issue ought to serve as a reference for students, researchers, and industry practitioners interested in the evolving, interdisciplinary area of intelligent computing in large-scale systems. We very much hope that readers will find in this compendium new inspiration and ideas to enhance their own research.

The concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.

This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites. Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards. Data in social networking websites is inherently unstructured and fuzzy in nature. In everyday life conversations, people do not care about the spellings and accurate grammatical construction of a sentence that may lead to different types of ambiguities, such as lexical, syntactic, and semantic. Therefore, analyzing and extracting information patterns from such data sets are more complex. Several surveys have been conducted to analyze different methods for the information extraction. Most of the surveys emphasized on the application of different text mining techniques for unstructured data sets reside in the form of text documents, but do not specifically target the data sets in social networking website. This survey attempts to provide a thorough understanding of different text mining techniques as well as the application of these techniques in the social networking websites. This survey investigates the recent advancement in the field of text analysis and covers two basic approaches of text mining, such as classification and clustering that are widely used for the exploration of the unstructured text available on the Web.

The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS). The paper starts with a general introduction describing the background, structure and behaviour of EMAS. EMAS application to solving global optimisation problems is presented in the next section along with its modification targeted at lowering the computation costs by early removing certain agents based on immunological inspirations. Subsequent sections deal with the elitist variant of EMAS aimed at solving multi-criteria optimisation problems, and the co-evolutionary one aimed at solving multi-modal optimisation problems. Each variation of EMAS is illustrated with selected experimental results.

Multi-agent systems have been widely used in the literature, including for the monitoring of distributed systems. However, one of the unresolved issues in this technology remains in the reassignment of the responsibilities of monitoring agents when some of them become unable to meet their obligations. This paper proposes a new approach for solving this problem based on (a) the gathering of evidence on whether the agent can or cannot fulfil the tasks it has been assigned and (b) the reassignment of the task to alternative agents using their trust level as a selection parameter. A weather station case study is proposed as an instantiation of the proposed model.

Biometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong and invasive biometric traits, such as fingerprint and iris, have been considered, analyzed, and combined in unimodal and multimodal biometric sensors. Biometric sensor performance has been evaluated using the well-known FVC2002, CASIA, and BATH databases.

The sources of information on the Web relating to Fine Art and in particular to Fine Artists are numerous, heterogeneous and distributed. Data relating to the biographies of an artist, images of their artworks, location of the artworks and exhibition reviews invariably reside in distinct and seemingly unrelated, or at least unlinked, sources. While communication and exchange exists, there is a great deal of independence between major repositories, such as museum, often owing to their ownership or heritage. This increases the individuality in the repository’s own processes and dissemination. It is currently necessary to browse through numerous different websites to obtain information about any one artist, and at this time there is little aggregation of Fine Art Information. This is in contrast to the domain of books and music, where the aggregation and re-grouping of information (usually by author or artist/band name) has become the norm. A Museum API (Application Programming Interface), however, is a tool that can facilitate a similar information service for the domain of Fine Art, by allowing the retrieval and aggregation of Web-based Fine Art Information, whilst at the same time increasing public access to the content of a museum’s collection. In this paper, we present the case for a pragmatic solution to the problems of heterogeneity and distribution of Fine Art Data and this is the first step towards the comprehensive re-presentation of Fine Art Information in a more ‘artist-centric’ way, via accessible Web applications. This paper examines the domain of Fine Art Information on the Web, putting forward the case for more Web services such as generic Museum APIs, highlighting this via a prototype Web application known as the ArtBridge. The generic Museum API is the standardisation mechanism to enable interfacing with specific Museum APIs.